A proposed solution to the problem of attenuating target signal components by inclusion into the mean noise estimate, is to simply remove all peak components that may constitute a potential target signal. This can be achieved using a CFAR detector, such as the DF-CFAR previously mentioned. Using the detector, potential signals can be identified and effectively removed from the noise estimate by flooring them to some scaled value of the CFAR detection threshold used. To ensure all potential components are successfully located, a very high false alarm probability is used to maximize sensitivity. By using a value much higher than that of the final target detection stage (performed after whitening), the inability to detect a source component and subsequent inclusion into the mean noise estimate will not affect the final detection performance. It is proposed that the OS-CFAR detector be utilized since this form offers computational simplicity and superior performance in multi-target environments [

15]. However, essentially any CFAR detector may be used instead. Some common forms include the cell-averaging CFAR (CA-CFAR) [

16], the greatest-of cell averaging CFAR (GOCA-CFAR) [

17], the smallest-of cell averaging CFAR (SOCA-CFAR) [

17], the ordered statistic CFAR (OS-CFAR) [

18], the censored mean level CFAR (CML-CFAR) [

19], and the trimmed mean CFAR (TM-CFAR) [

20]. Each of these detectors operate using the same principles, with differences only in the method in which the reference noise level is determined. For the OS-CFAR detector, the following binary testing function may be constructed:

where

$\eta \left(f,w\right)$ is the threshold factor given by

where

${\alpha}_{os}$ is the order statistic scaling factor, and

$\left|{X}_{k}\left(f,w\right)\right|$ is the

${k}^{th}$ largest spectral component contained in the noise sample bandwidth of size

$N$ taken about the test cell

$\left|X\left(f,w\right)\right|$.

Prior to calculating the mean approximation, potential signal components are effectively removed by flooring their value to some scaled fraction of the detection threshold used. This can be expressed by the following operation:

where

$\delta $ is the flooring scale factor. The mean approximation is then found by substituting the above value into Equations (4)–(6). Finally, the spectrally whitened form can then be obtained via Equations (1) and (2) with

$\gamma =0$.